Types of data

• Quantitative data


  Numerical data that is statistically analysed to identify patterns and make comparisons
• Quantitative data


  • Time taken to run 100m
  • Number of items recalled in a memory test
  • Score on an IQ test
• How quantitative data is obtained

  • Administering closed, rating-style questions in a questionnaire
  • Tallying behavioural categories in an observation
  • Conducting a content analysis of interviews
• Question producing quantitative data

  • How many hours do you spend on Instagram a day?
• Quantitative data


  • Lacks detail
  • Numerical data cannot be elaborated on
• Lack of detail in quantitative data

  May mean less meaningful conclusions are drawn
• Quantitative data

  • Analysis is objective
  • Relies on numerical data that does not need to be subjectively interpreted
• Objective analysis of quantitative data

  Researchers are more likely to draw non-biased conclusions
• Quantitative data


  • Easy to analyse
  • Numerical data can be quickly categorised, summarised and compared
• Easy analysis of quantitative data


  Speeds up the research process and allows for more easy comparison of data within and between studies
• Qualitative data


  Non-numerical, descriptive data that uses words to give a full description of what people think or feel
• Qualitative data


  • A person experiencing depression describing their symptoms
• How qualitative data is obtained


  • Using open questions in a questionnaire
  • Using interviews
  • Conducting a thematic analysis
• Question producing qualitative data


  • Why might scrolling-through Instagram make someone feel sad?
• Qualitative data

  • In-depth
  • Descriptive data enables participants to elaborate on their responses
• In-depth qualitative data


  May mean that more meaningful conclusions are drawn
• Qualitative data


  • Analysis is subjective
  • Relies on researchers having to interpret descriptive data
• Subjective analysis of qualitative data

  Researchers may draw biased conclusions
• Qualitative data


  • Difficult to analyse
  • Descriptive data can result in a variety of individual responses that are difficult to summarise and compare
• Difficulty analysing qualitative data


  Slows down the research process and makes it difficult to compare data within and between studies
• Primary data


  Data that is gathered directly from the participants and is collected specifically for the purpose of the current investigation
• Methods to obtain primary data

  • Experiment
  • Observation
  • Interview
  • Questionnaire
  • Case study
• Strengths of primary data

  • More likely to gather the required information
  • Likely to lead to greater insight
  • Can be controlled by the researcher
• Limitations of primary data

  • Can be time consuming and costly
• Secondary data


  Data that is not gathered directly from participants, instead it is pre-existing data that was not originally collected for the purpose of the current investigation
• Sources of secondary data

  • Journal articles (research reports)
  • Internet sources
  • Newspapers
  • Company documents
• Limitations of secondary data

  • Less likely to gather the required information
  • Less likely to provide insightful detail
  • Cannot be controlled by the researcher
• Strengths of secondary data

  • Can be less time consuming and costly
• Meta-analysis


  An example of secondary data where other researchers' findings from a series of studies are collected and analysed to present an overall result
• Strengths of meta-analyses


  • Conclusions are typically drawn from large sample sizes
• Limitations of meta-analyses


  • May be affected by confounding variables